Abstract
This paper presents practical implementation of the equiconvergent learning algorithm for blind source separation. The equiconvergent algorithm [4] has favorite properties such as isotropic convergence and universal convergence, but it requires to estimate unknown activation functions and certain unknown statistics of source signals. The estimation of such activation functions and statistics becomes critical in realizing the equi-convergent algorithm. It is the purpose of this paper to develop a new approach to estimate the activation functions adaptively for blind source separation. We propose the exponential type family as a model for probability density functions. A method of constructing an exponential family from the activation (score) functions is proposed. Then, a learning rule based on the maximum likelihood is derived to update the parameters in the exponential family. The learning rule is compatible with minimization of mutual information for training demixing models. Finally, computer simulations are given to demonstrate the effectiveness and validity of the proposed approach.
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Zhang, L.Q., Amari, S., Cichocki, A. (2001). Equi-convergence Algorithm for Blind Separation of Sources with Arbitrary Distributions. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_100
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DOI: https://doi.org/10.1007/3-540-45723-2_100
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